Bootstrap based probability forecasting in multiplicative error models

Perera, Indeewara; Silvapulle, Mervyn J.

doi:10.1016/j.jeconom.2020.01.022

Cited by 7 publications

(6 citation statements)

References 55 publications

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“…where M n is a random variable that converges to zero with probability one. Here we have used some arguments from the proof of Lemma 6 in Perera and Silvapulle (2021). Furthermore, for some K < ∞, we have that…”

Section: A2 Some Preliminary Resultsmentioning

confidence: 99%

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Specification tests for GARCH processes

Cavaliere¹,

Perera²,

Rahbek³

2021

Preprint

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This paper develops tests for the correct specification of the conditional variance function in GARCH models when the true parameter may lie on the boundary of the parameter space. The test statistics considered are of Kolmogorov-Smirnov and Cramér-von Mises type, and are based on a certain empirical process marked by centered squared residuals. The limiting distributions of the test statistics are not free from (unknown) nuisance parameters, and hence critical values cannot be tabulated. A novel bootstrap procedure is proposed to implement the tests; it is shown to be asymptotically valid under general conditions, irrespective of the presence of nuisance parameters on the boundary. The proposed bootstrap approach is based on shrinking of the parameter estimates used to generate the bootstrap sample toward the boundary of the parameter space at a proper rate. It is simple to implement and fast in applications, as the associated test statistics have simple closed form expressions. A simulation study demonstrates that the new tests: (i) have excellent finite sample behaviour in terms of empirical rejection probabilities under the null as well as under the alternative; (ii) provide a useful complement to existing procedures based on Ljung-Box type approaches. Two data examples are considered to illustrate the tests.

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Section: A2 Some Preliminary Resultsmentioning

confidence: 99%

“…, is assumed to be fixed for the statistical analysis. The asymptotic results do not change if ς 0 is replaced by an arbitrarily chosen vector (e.g., by setting Y t = 0 and h t = 0, all t ≤ 0); see, for example, the discussions in Straumann and Mikosch (2006), Perera and Silvapulle (2021) and Jensen and Rahbek (2004).…”

Section: Formulation Of the Problemmentioning

confidence: 99%

Specification tests for GARCH processes

Cavaliere¹,

Perera²,

Rahbek³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Specifically, we show that our recursive bootstrap corresponds to a residual-based bootstrap in the ACD world (as discussed e.g. in Perera and Silvapulle, 2021), with the crucial difference that the number of events generated through our scheme is random, rather than being fixed. This is a key improvement, as our bootstrap ensures that the sum of the bootstrap waiting times always cover the original time interval.…”

Section: Introductionmentioning

confidence: 83%

Bootstrap inference for Hawkes and general point processes

Cavaliere

Lü

Rahbek

et al. 2023

Journal of Econometrics

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“…Some of the key features of QML and GMM estimators have been discussed in Pacurar (2008), Hautsch (2012), Brownlees et al (2012), Cipollini et al (2013), and Perera et al (2016), among others. Although these estimators have desirable asymptotic properties, their finite sample performance can at times be sensitive to the (unknown) conditional distribution of the observable process, and hence, in practice, when available, fully efficient maximum likelihood (ML) estimates are often preferred (see Grammig and Maurer, 2000;Perera and Silvapulle, 2021). For example, even though the asymptotic distribution of the QMLE is independent of the innovation distribution, when the data generating process is based on an innovation distribution that induces a non-monotonic hazard rate function (e.g.…”

Section: Introductionmentioning

confidence: 99%

A class of Minimum Distance Estimators in Markovian Multiplicative Error Models

2022

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This paper proposes a class of minimum distance estimators for the underlying parameters in a Markovian parametric multiplicative error time series model. This class of estimators is based on the integrals of the square of a certain marked residual process. The paper derives the asymptotic distributions of the proposed estimators. In a finite sample comparison, some members of the proposed class of estimators dominate a generalized method of moments estimator in terms of the finite sample bias at a variety of chosen error distributions while neither dominate each other in terms of the mean squared error at these error distributions. A real data example is considered to illustrate the proposed estimation procedures.

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Bootstrap based probability forecasting in multiplicative error models

Cited by 7 publications

References 55 publications

Specification tests for GARCH processes

Specification tests for GARCH processes

Bootstrap inference for Hawkes and general point processes

A class of Minimum Distance Estimators in Markovian Multiplicative Error Models

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